Visualization for Architecture, Engineering, and Construction: Shaping the Future of Our Built World

Abdelaal, Moataz; Amtsberg, Felix; Becher, Michael; Estrada, Rebeca Duque; Kannenberg, Fabian; Calepso, Aimee Sousa; Wagner, Hans Jakob; Reina, Guido; Sedlmair, Michael; Menges, Achim; Weiskopf, Daniel

doi:10.1109/mcg.2022.3149837

Cited by 15 publications

(3 citation statements)

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“…Since the amounts of possible solutions are vast, predetermined agent behaviours can be used to find initial valid fibre layups. Visual analytics can aid in the assessment and expose relationships between input parameters and the evaluation criteria (Abdelaal et al, 2022).…”

Section: Artificial Intelligence For Self-learning Fibre Agentsmentioning

confidence: 99%

Computational co-design of fibrous architecture

Menges

Kannenberg

Zechmeister

2022

ARIN

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Fibrous architecture constitutes an alternative approach to conventional building systems and established construction methods. It shows the potential to converge architectural concerns such as spatial expression and structural elegance, with urgently required resource effectiveness and material efficiency, in a genuinely computational approach. Fundamental characteristics of fibre composite are shared with fibre structures in the natural world, enabling the transfer of design principles and providing a vast repertoire of inspiration. Robotic fabrication based on coreless filament winding, a technique to deposit resin impregnated fibre filaments with only minimal formwork, as well as integrative computational design methods are imperative to the development of complex fibrous building systems. Two projects, the BUGA Fibre Pavilion as an example for long-span structures, and Maison Fibre as an example of multi-storey architecture, showcase the application of those techniques in an architectural context and highlight areas of further research opportunities. The highly interrelated aesthetic, structural and fabrication characteristics of fibre nets are difficult to understand and go beyond a designer’s comprehension and intuition. An AI powered, self-learning agent system aims to extend and thoroughly explore the design space of fibre structures to unlock the full design potential coreless filament winding offers. In order to ensure feedback between all relevant design and performance criteria and enable interdisciplinary convergence, these novel design methods are embedded in a larger co-design framework. It formalizes the interaction of involved interdisciplinary domains and allows for interactive collaboration based on a central data model, serving as a base for design optimisation and exploration. To further advance research on fibre composites in architecture, bio-based materials are considered, continuing the journey of discovery of fibrous architecture to fundamentally rethinking design and construction towards a novel, computational material culture in architecture.

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Section: Artificial Intelligence For Self-learning Fibre Agentsmentioning

confidence: 99%

Computational co-design of fibrous architecture

Menges

Kannenberg

Zechmeister

2022

ARIN

Self Cite

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“…Computer graphics rendering plays a crucial role in various fields in today's digital era, including game development, movie special effects, virtual reality, and architectural design [1][2][3][4]. With the continuous advancement of graphics rendering and the increasing demand, optimizing the performance of graphics processing units (GPUs) has become…”

Section: Introductionmentioning

confidence: 99%

RayBench: An Advanced NVIDIA-Centric GPU Rendering Benchmark Suite for Optimal Performance Analysis

Wang,

2023

Electronics

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This study aims to collect GPU rendering programs and analyze their characteristics to construct a benchmark dataset that reflects the characteristics of GPU rendering programs, providing a reference basis for designing the next generation of graphics processors. The research framework includes four parts: GPU rendering program integration, data collection, program analysis, and similarity analysis. In the program integration and data collection phase, 1000 GPU rendering programs were collected from open-source repositories, and 100 representative programs were selected as the initial benchmark dataset. The program analysis phase involves instruction-level, thread-level, and memory-level analysis, as well as five machine learning algorithms for importance ranking. Finally, through Pearson similarity analysis, rendering programs with high similarity were eliminated, and the final GPU rendering program benchmark dataset was selected based on the benchmark’s comprehensiveness and representativeness. The experimental results of this study show that, due to the need to load and process texture and geometry data in rendering programs, the average global memory access efficiency is generally lower compared to the averages of the Rodinia and Parboil benchmarks. The GPU occupancy rate is related to the computationally intensive tasks of rendering programs. The efficiency of stream processor execution and thread bundle execution is influenced by branch statements and conditional judgments. Common operations such as lighting calculations and texture sampling in rendering programs require branch judgments, which reduce the execution efficiency. Bandwidth utilization is improved because rendering programs reduce frequent memory access and data transfer to the main memory through data caching and reuse. Furthermore, this study used multiple machine learning methods to rank the importance of 160 characteristics of 100 rendering programs on four different NVIDIA GPUs. Different methods demonstrate robustness and stability when facing different data distributions and characteristic relationships. By comparing the results of multiple methods, biases inherent to individual methods can be reduced, thus enhancing the reliability of the results. The contribution of this study lies in the analysis of workload characteristics of rendering programs, enabling targeted performance optimization to improve the efficiency and quality of rendering programs. By comprehensively collecting GPU rendering program data and performing characteristic analysis and importance ranking using machine learning methods, reliable reference guidelines are provided for GPU design. This is of significant importance in driving the development of rendering technology.

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“…AR technology superimposes digital images on the real-world environment which can be viewed through devices such as smartphones. Architects have been able to utilize this technology to plan their projects and visualize how they will appear in actual settings and in archelogy with the use of AR, it has enhanced tourism to assist in interacting with historical archaeological features [14], [15].…”

Section: Ar In Educationmentioning

confidence: 99%